{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generative Adversarial Networks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook we'll see how to build a very popular type of deep learning model called a generative adversarial network, or GAN for short.  Interest in GANs has exploded in the last few years thanks to their unique ability to generate complex human-like results.  GANs have been used for image synthesis, style transfer, music generation, and many other novel applications.  We'll build a particular type of GAN called the Deep Convolutional GAN (DCGAN) from scratch and train it to generate MNIST images.\n",
    "\n",
    "Much of the code used in this notebook is adapted from the excellent [Keras-GAN](https://github.com/eriklindernoren/Keras-GAN) library created by Erik Linder-Noren.\n",
    "\n",
    "Let's get started by importing a few libraries and loading up MNIST into an array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from keras.datasets import mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://s3.amazonaws.com/img-datasets/mnist.npz\n",
      "11493376/11490434 [==============================] - 1s 0us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28, 1)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(X, _), (_, _) = mnist.load_data()\n",
    "X = X / 127.5 - 1.\n",
    "X = np.expand_dims(X, axis=3)\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we've added another dimension for the channel which is usually for the color value (red, green, blue).  Since these images are greyscale it's not strictly necessary but Keras expects an input array in this shape.\n",
    "\n",
    "The first thing we need to do is define the generator.  This is the model that will learn how to produce new images.  The architecture looks a lot like a normal convolutional network except in reverse.  We start with an input that is essentially random noise and mold it into an image through a series of convolution blocks.  All of these layers should be familiar to anyone that's worked with convolutional networks before with the possible exception of the \"upsampling\" layer, which essentially doubles the size of the tensor along two axes by repeating rows and columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Model\n",
    "from keras.layers import Activation, BatchNormalization, Conv2D\n",
    "from keras.layers import Dense, Input, Reshape, UpSampling2D\n",
    "\n",
    "def get_generator(noise_shape):\n",
    "    i = Input(noise_shape)\n",
    "\n",
    "    x = Dense(128 * 7 * 7)(i)\n",
    "    x = Activation(\"relu\")(x)\n",
    "    x = Reshape((7, 7, 128))(x)\n",
    "    x = UpSampling2D()(x)\n",
    "\n",
    "    x = Conv2D(128, kernel_size=3, padding=\"same\")(x)\n",
    "    x = BatchNormalization(momentum=0.8)(x)\n",
    "    x = Activation(\"relu\")(x)\n",
    "    x = UpSampling2D()(x)\n",
    "\n",
    "    x = Conv2D(64, kernel_size=3, padding=\"same\")(x)\n",
    "    x = BatchNormalization(momentum=0.8)(x)\n",
    "    x = Activation(\"relu\")(x)\n",
    "\n",
    "    x = Conv2D(1, kernel_size=3, padding=\"same\")(x)\n",
    "    o = Activation(\"tanh\")(x)\n",
    "    \n",
    "    return Model(i, o)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It helps to see the summary output for the model and follow along as the shape changes.  One interesting thing to note is how many of the parameters in the model are in that initial dense layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 6272)              633472    \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 6272)              0         \n",
      "_________________________________________________________________\n",
      "reshape_1 (Reshape)          (None, 7, 7, 128)         0         \n",
      "_________________________________________________________________\n",
      "up_sampling2d_1 (UpSampling2 (None, 14, 14, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 14, 14, 128)       147584    \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 14, 14, 128)       512       \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 14, 14, 128)       0         \n",
      "_________________________________________________________________\n",
      "up_sampling2d_2 (UpSampling2 (None, 28, 28, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 28, 28, 64)        73792     \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 28, 28, 64)        256       \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 28, 28, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 28, 28, 1)         577       \n",
      "_________________________________________________________________\n",
      "activation_4 (Activation)    (None, 28, 28, 1)         0         \n",
      "=================================================================\n",
      "Total params: 856,193\n",
      "Trainable params: 855,809\n",
      "Non-trainable params: 384\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "g = get_generator(noise_shape=(100,))\n",
    "g.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next up is the discriminator.  This model will be tasked with looking at an image and deciding if it's really an MNIST image or if it's a fake.  The discriminator looks a lot more like a standard convolutional network.  It takes a tensor of an image as it's input and runs it through a series of convolution blocks, decreasing in size until it outputs a single activation representing the probability of the tensor being a real MNIST image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Dropout, Flatten, LeakyReLU, ZeroPadding2D\n",
    "\n",
    "def get_discriminator(img_shape):\n",
    "    i = Input(img_shape)\n",
    "    \n",
    "    x = Conv2D(32, kernel_size=3, strides=2, padding=\"same\")(i)\n",
    "    x = LeakyReLU(alpha=0.2)(x)\n",
    "    x = Dropout(0.25)(x)\n",
    "\n",
    "    x = Conv2D(64, kernel_size=3, strides=2, padding=\"same\")(i)\n",
    "    x = ZeroPadding2D(padding=((0, 1), (0, 1)))(x)\n",
    "    x = BatchNormalization(momentum=0.8)(x)\n",
    "    x = LeakyReLU(alpha=0.2)(x)\n",
    "    x = Dropout(0.25)(x)\n",
    "\n",
    "    x = Conv2D(128, kernel_size=3, strides=2, padding=\"same\")(x)\n",
    "    x = BatchNormalization(momentum=0.8)(x)\n",
    "    x = LeakyReLU(alpha=0.2)(x)\n",
    "    x = Dropout(0.25)(x)\n",
    "\n",
    "    x = Conv2D(256, kernel_size=3, strides=1, padding=\"same\")(x)\n",
    "    x = BatchNormalization(momentum=0.8)(x)\n",
    "    x = LeakyReLU(alpha=0.2)(x)\n",
    "    x = Dropout(0.25)(x)\n",
    "    \n",
    "    x = Flatten()(x)\n",
    "    o = Dense(1)(x)\n",
    "    o = Activation(\"sigmoid\")(o)\n",
    "    \n",
    "    return Model(i, o)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_2 (InputLayer)         (None, 28, 28, 1)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 14, 14, 64)        640       \n",
      "_________________________________________________________________\n",
      "zero_padding2d_1 (ZeroPaddin (None, 15, 15, 64)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 15, 15, 64)        256       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_2 (LeakyReLU)    (None, 15, 15, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 15, 15, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 8, 8, 128)         73856     \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 8, 8, 128)         512       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_3 (LeakyReLU)    (None, 8, 8, 128)         0         \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 8, 8, 128)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 8, 8, 256)         295168    \n",
      "_________________________________________________________________\n",
      "batch_normalization_5 (Batch (None, 8, 8, 256)         1024      \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_4 (LeakyReLU)    (None, 8, 8, 256)         0         \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 8, 8, 256)         0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 16384)             0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 16385     \n",
      "_________________________________________________________________\n",
      "activation_5 (Activation)    (None, 1)                 0         \n",
      "=================================================================\n",
      "Total params: 387,841\n",
      "Trainable params: 386,945\n",
      "Non-trainable params: 896\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "d = get_discriminator(img_shape=(28, 28, 1))\n",
    "d.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With both components defined, we can now bring it all together and create the GAN model.  It's worth spending some time examining this part closely as it can be confusing at first.  Notice that we first compile the distriminator by itself.  Then we define the generator and feed its output to the discriminator, but only after setting the discriminator so that training is disabled.  This is because we don't want the discriminator to update its weights during this part, only the generator.  Finally, a third model is created combining the original input to the generator and the output of the discriminator.  Don't worry if you find this confusing, it takes some time to fully comprehend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.optimizers import Adam\n",
    "\n",
    "def DCGAN():\n",
    "    optimizer = Adam(lr=0.0002, beta_1=0.5)\n",
    "    discriminator = get_discriminator(img_shape=(28, 28, 1))\n",
    "    discriminator.compile(loss=\"binary_crossentropy\", optimizer=optimizer, metrics=[\"accuracy\"])\n",
    "    generator = get_generator(noise_shape=(100,))\n",
    "    z = Input(shape=(100,))\n",
    "    g_out = generator(z)\n",
    "    discriminator.trainable = False\n",
    "    d_out = discriminator(g_out)\n",
    "    combined = Model(z, d_out)\n",
    "    combined.compile(loss=\"binary_crossentropy\", optimizer=optimizer)\n",
    "    \n",
    "    return generator, discriminator, combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_5 (InputLayer)         (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "model_4 (Model)              (None, 28, 28, 1)         856193    \n",
      "_________________________________________________________________\n",
      "model_3 (Model)              (None, 1)                 387841    \n",
      "=================================================================\n",
      "Total params: 1,244,034\n",
      "Trainable params: 855,809\n",
      "Non-trainable params: 388,225\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "generator, discriminator, combined = DCGAN()\n",
    "combined.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a function to save the generator's output so we can visually see the results.  We'll use this in the training loop below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_image(generator, batch):\n",
    "    r, c = 5, 5\n",
    "    noise = np.random.normal(0, 1, (r * c, 100))\n",
    "    gen_images = generator.predict(noise)\n",
    "    gen_images = 0.5 * gen_images + 0.5\n",
    "\n",
    "    fig, axs = plt.subplots(r, c)\n",
    "    cnt = 0\n",
    "    for i in range(r):\n",
    "        for j in range(c):\n",
    "            axs[i, j].imshow(gen_images[cnt, :, :, 0], cmap=\"gray\")\n",
    "            axs[i, j].axis('off')\n",
    "            cnt += 1\n",
    "    fig.savefig(\"mnist_%d.png\" % (batch))\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last challenge with building a GAN is training the whole thing.  With something this complex we can't just call the standard fit function because there are multiple steps involed.  Let's roll our own and see how the algorithm works.  Start by generating some random noise and feeding it through the generator to produce a batch of \"fake\" images.  Next, select a batch of real images from the training data.  Now train the distriminator on both the fake image batch and the real image batch, setting labels appropriately (0 for fake and 1 for real).  Finally, train the generator by feeding the \"combined\" model a batch of noise with the \"real\" label.\n",
    "\n",
    "You may find this last step confusing.  Remember what this \"combined\" model is doing.  It runs the input (random noise) through the generator, then runs that output through the discriminator (which is frozen), and gets a loss.  What this ends of doing in essence is saying \"train the generator to minimize the discriminator's ability to discern this output from a real image\".  This has the effect, over time, of causing the generator to produce realistic images!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(generator, discriminator, combined, X, batch_size, n_batches):\n",
    "    t0 = time.time()\n",
    "    for batch in range(n_batches + 1):\n",
    "        t1 = time.time()\n",
    "        noise = np.random.normal(0, 1, (batch_size, 100))\n",
    "        \n",
    "        # Create fake images\n",
    "        fake_images = generator.predict(noise)\n",
    "        fake_labels = np.zeros((batch_size, 1))\n",
    "\n",
    "        # Select real images\n",
    "        idx = np.random.randint(0, X.shape[0], batch_size)\n",
    "        real_images = X[idx]\n",
    "        real_labels = np.ones((batch_size, 1))\n",
    "\n",
    "        # Train the discriminator\n",
    "        d_loss_real = discriminator.train_on_batch(real_images, real_labels)\n",
    "        d_loss_fake = discriminator.train_on_batch(fake_images, fake_labels)\n",
    "        d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)\n",
    "\n",
    "        # Train the generator\n",
    "        noise = np.random.normal(0, 1, (batch_size, 100))\n",
    "        g_loss = combined.train_on_batch(noise, real_labels)\n",
    "        t2 = time.time()\n",
    "\n",
    "        # Report progress\n",
    "        if batch % 100 == 0:\n",
    "            print(\"Batch %d/%d [Batch time: %.2f s, Total: %.2f m] [D loss: %f, Acc: %.2f%%] [G loss: %f]\" %\n",
    "                  (batch, (n_batches), (t2 - t1), ((t2 - t0) / 60), d_loss[0], 100 * d_loss[1], g_loss))\n",
    "        if batch % 500 == 0:\n",
    "            save_image(generator, batch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the training loop for a while.  We can't read too much into the losses reported by this process because the models are competing with each other.  They both end up being pretty stable over the training period but it doesn't really capture what's going on because both models are getting better at their task by roughly equal amounts.  If either model went to zero loss that would likely indicate there's a problem that needs fixed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Batch 0/3000 [Batch time: 0.03 s, Total: 0.00 m] [D loss: 0.687554, Acc: 62.50%] [G loss: 1.088619]\n",
      "Batch 100/3000 [Batch time: 0.03 s, Total: 0.05 m] [D loss: 0.937827, Acc: 50.00%] [G loss: 1.534353]\n",
      "Batch 200/3000 [Batch time: 0.04 s, Total: 0.10 m] [D loss: 0.890954, Acc: 46.88%] [G loss: 1.568295]\n",
      "Batch 300/3000 [Batch time: 0.03 s, Total: 0.16 m] [D loss: 0.757705, Acc: 48.44%] [G loss: 1.378710]\n",
      "Batch 400/3000 [Batch time: 0.03 s, Total: 0.21 m] [D loss: 0.752167, Acc: 53.12%] [G loss: 1.317879]\n",
      "Batch 500/3000 [Batch time: 0.03 s, Total: 0.26 m] [D loss: 0.792607, Acc: 46.88%] [G loss: 1.668158]\n",
      "Batch 600/3000 [Batch time: 0.03 s, Total: 0.31 m] [D loss: 0.681811, Acc: 65.62%] [G loss: 1.534778]\n",
      "Batch 700/3000 [Batch time: 0.03 s, Total: 0.36 m] [D loss: 0.787874, Acc: 45.31%] [G loss: 1.265980]\n",
      "Batch 800/3000 [Batch time: 0.03 s, Total: 0.41 m] [D loss: 0.720734, Acc: 51.56%] [G loss: 1.346431]\n",
      "Batch 900/3000 [Batch time: 0.03 s, Total: 0.46 m] [D loss: 0.816940, Acc: 48.44%] [G loss: 1.155806]\n",
      "Batch 1000/3000 [Batch time: 0.03 s, Total: 0.51 m] [D loss: 0.793951, Acc: 42.19%] [G loss: 1.316577]\n",
      "Batch 1100/3000 [Batch time: 0.03 s, Total: 0.56 m] [D loss: 0.710668, Acc: 54.69%] [G loss: 1.433707]\n",
      "Batch 1200/3000 [Batch time: 0.03 s, Total: 0.61 m] [D loss: 0.822841, Acc: 45.31%] [G loss: 1.055463]\n",
      "Batch 1300/3000 [Batch time: 0.03 s, Total: 0.66 m] [D loss: 0.572396, Acc: 71.88%] [G loss: 1.093200]\n",
      "Batch 1400/3000 [Batch time: 0.03 s, Total: 0.71 m] [D loss: 0.753564, Acc: 50.00%] [G loss: 1.097862]\n",
      "Batch 1500/3000 [Batch time: 0.03 s, Total: 0.76 m] [D loss: 0.617892, Acc: 67.19%] [G loss: 0.939282]\n",
      "Batch 1600/3000 [Batch time: 0.03 s, Total: 0.81 m] [D loss: 0.647099, Acc: 59.38%] [G loss: 1.145073]\n",
      "Batch 1700/3000 [Batch time: 0.03 s, Total: 0.86 m] [D loss: 0.686766, Acc: 59.38%] [G loss: 1.009623]\n",
      "Batch 1800/3000 [Batch time: 0.03 s, Total: 0.91 m] [D loss: 0.757178, Acc: 43.75%] [G loss: 1.162026]\n",
      "Batch 1900/3000 [Batch time: 0.03 s, Total: 0.95 m] [D loss: 0.626452, Acc: 62.50%] [G loss: 1.149147]\n",
      "Batch 2000/3000 [Batch time: 0.03 s, Total: 1.00 m] [D loss: 0.706682, Acc: 54.69%] [G loss: 1.052020]\n",
      "Batch 2100/3000 [Batch time: 0.03 s, Total: 1.05 m] [D loss: 0.783831, Acc: 57.81%] [G loss: 1.271681]\n",
      "Batch 2200/3000 [Batch time: 0.03 s, Total: 1.10 m] [D loss: 0.630528, Acc: 65.62%] [G loss: 1.128464]\n",
      "Batch 2300/3000 [Batch time: 0.02 s, Total: 1.15 m] [D loss: 0.693636, Acc: 57.81%] [G loss: 1.398235]\n",
      "Batch 2400/3000 [Batch time: 0.03 s, Total: 1.19 m] [D loss: 0.735144, Acc: 51.56%] [G loss: 1.137753]\n",
      "Batch 2500/3000 [Batch time: 0.03 s, Total: 1.24 m] [D loss: 0.599575, Acc: 62.50%] [G loss: 0.984566]\n",
      "Batch 2600/3000 [Batch time: 0.03 s, Total: 1.29 m] [D loss: 0.774844, Acc: 51.56%] [G loss: 1.256624]\n",
      "Batch 2700/3000 [Batch time: 0.03 s, Total: 1.34 m] [D loss: 0.708373, Acc: 51.56%] [G loss: 1.276029]\n",
      "Batch 2800/3000 [Batch time: 0.03 s, Total: 1.39 m] [D loss: 0.641630, Acc: 60.94%] [G loss: 1.084028]\n",
      "Batch 2900/3000 [Batch time: 0.03 s, Total: 1.44 m] [D loss: 0.612454, Acc: 65.62%] [G loss: 1.232962]\n",
      "Batch 3000/3000 [Batch time: 0.03 s, Total: 1.49 m] [D loss: 0.706665, Acc: 53.12%] [G loss: 1.106143]\n"
     ]
    }
   ],
   "source": [
    "train(generator, discriminator, combined, X=X, batch_size=32, n_batches=3000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take a look at the generator's output at various stages of training.  In the beginning it's clear that the model is producing noise, but after just a few hundred batches it's already making progress.  After a couple thousand batches the images look quite realistic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import Image, display\n",
    "\n",
    "names = [\"mnist_0.png\",\n",
    "         \"mnist_500.png\",\n",
    "         \"mnist_1000.png\",\n",
    "         \"mnist_1500.png\",\n",
    "         \"mnist_2000.png\",\n",
    "         \"mnist_2500.png\",\n",
    "         \"mnist_3000.png\"]\n",
    "\n",
    "for name in names:\n",
    "    display(Image(filename=name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
